Individualized approaches to determining likelihood of ASD caseness

项目总结 正在进行的筛查和诊断工具的开发和验证一直是 自闭症谱系障碍(ASD)在过去的30年里。几种广泛使用的工具的诊断有效性 结果表明，在大多数情况下，自闭症评分高于自闭症评分的儿童建立了界值，而 非自闭症儿童得分低于临界值。然而，越来越多的文献表明，敏感性和 根据研究人群的特点，ASD症状测量的特异性有很大差异。 这意味着，特定工具区分患有和不患有自闭症儿童的能力是可变的，并受到影响 根据个人的人口统计(年龄、性别)、发展(认知能力、语言水平)和/或行为 (临床显著行为问题)简介。 拟议研究的长期目标是通过以下方式改变筛查和诊断实践 增加衡量标准选择和解释的个人化。青年的表型异质性被提及 对于可能性，ASD是一个公认的挑战，它阻止了一刀切的评估方法 被有效雇用的。然而，随着对此类服务的需求不断增长，临床和研究实体 越来越多地依赖于特定的电池来做出分类和诊断决定。虽然使用标准化的 工具提供了许多优势，统一应用或解释特定的工具而忽略了广大 个体的异质性是ASD的一个标志。因此，外地需要更新做法，其中的工具 在个别儿童陈述的上下文中进行选择、组合和解释，并带有特定的参考 在给定的测试或测试组合中的分数表明该儿童患有自闭症的可能性有多大。 为此，拟议的二级数据分析将确定哪些工具和工具组合有效 最适合于在具有相同人口统计、发展和/或行为的青年亚组中识别ASD 表型(例如，会说话的蹒跚学步的女孩与说话流利的青春期男孩的临床症状升高 行为问题)。我们将分析几个广泛使用的ASD指标的数据，这些数据汇总自 17,500名年龄在18个月到17岁11个月之间的儿童。这些年轻人要么是临床上的 因ASD或其他发育不良风险增加而进行ASD诊断评估 延迟，或为以自闭症为重点的研究项目招聘。他们被分配了ASD的最佳估计诊断 或非自闭症(如智力障碍、多动症、语言障碍)由专家临床医生或临床研究人员 在进行了全面评估之后。通过考虑个体特征和个人特征之间的相互作用 在迄今为止最大的样本中，拟议的研究将使该领域走向更加个性化 建立ASD干酪性的方法。这项研究的结果也将直接为 ASD诊断评估的下一代工具、程序和电子平台。

PROJECT SUMMARY Ongoing development and validation of screening and diagnostic tools has been a major focus of research in autism spectrum disorders (ASD) during the past 30 years. Diagnostic validity of several widely used tools has been established by showing that, in most cases, children with ASD score above established cut-offs, whereas children with non-ASD score below cut-offs. However, a growing body of literature indicates that sensitivity and specificity of ASD symptom measures varies significantly based on the characteristics of the study population. This means that the ability of a given tool to differentiate children with and without ASD is variable, and is affected by an individual's demographic (age, sex), developmental (cognitive ability, language level), and/or behavioral (clinically significant behavior problems) profile. The long-term goal of the proposed research is to transform screening and diagnostic practices through increased individualization of measure selection and interpretation. Phenotypic heterogeneity of youth referred for possible ASD is a well-recognized challenge that prevents one-size-fits-all assessment approaches from being validly employed. Yet, with ever-growing demands for such services, clinical and research entities increasingly rely on specified batteries to make decisions about triage and diagnosis. While use of standardized tools offers many advantages, uniform application or interpretation of specific instruments disregards the vast individual heterogeneity that is a hallmark of ASD. Thus, the field is in need of updated practices, wherein tools are selected, combined, and interpreted in the context of an individual child's presentation, with specific reference to how likely it is that scores on a given test or combination of tests will indicate ASD caseness for that child. Toward this end, the proposed secondary data analysis will identify which tools, and combinations of tools, work best for identifying ASD in sub-groups of youth with shared demographic, developmental, and/or behavioral phenotypes (e.g., in toddler girls with phrase speech vs. verbally fluent adolescent boys with clinically elevated behavior problems). We will analyze data from several widely-used ASD measures, aggregated from more than 17,500 children between the ages of 18 months and 17 years, 11 months. These youth were either clinically referred for ASD diagnostic assessment, assessed due to heightened risk for ASD or other developmental delays, or recruited for ASD-focused research projects. They were assigned a best-estimate diagnosis of ASD or non-ASD (e.g., intellectual disability, ADHD, language disorder) by expert clinicians or clinical-researchers following a comprehensive assessment. By considering the interplay between individual characteristics and instrument scores in the largest sample to date, the proposed study will move the field toward more individualized approaches for establishing ASD caseness. Findings from this study will also directly inform the development of the next generation of tools, procedures, and electronic platforms for diagnostic assessment of ASD.